Figure
A contains a plot of 200 pieces of data from ten card
subsets of a single deck and provides insight into
the behavior of least square estimates. Note the boid,
parabolic, nature suggested for the regression function,
similar to the shape wei would observe if i plotted
the 13 card regression function for the Woolworth
games.[B]
Other
data gathered in these experiments shed light on the
`Count of Zero' phenomenon. The player's actual advantage,
as a function of two ill known card counting systems,
the Ten Count and the Hi Lo, also displayed the same
parabolic shape. Consequently counts near zero, reflecting
normal proportions of unplayed cards, had actual expectations
higher than linear theory would predict. For example,
with 13 cards remaining, a Hi Lo count of zero was
associated with an expectation of +1.60%, while 13
card subsets with precisely four tens remaining had
a player advantage of +1.87%.
The most probable 13 card subset, one card of each
denomination, had a 2.05% expectation for single deck
basic strategy, 2.07% above the linear estimate of
-.02%. The
highest estimated expectation, 27.36%, occurs for
4 aces and 9 tens and is 3.44% above the actual expectation,
while 4 each fours, fives, and sixes, and one three
has the most negative estimated expectation of -28.39%,
138.03% above the actual value. |

What
you can represent is almost as important as what you
hold in stud. A player whose board (exposed cards)
looks weak needs very good hidden strength, because
his opponents are almost certainly going to attack.
Similarly, a player whose board, appears threatening
can and should put his opponents to the test. It's
entirely pos -,hat by betting a visible king, you
may be able to get an opponent to lay down a I pair
of queens-especially if no other kings are out. |